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Moazami Goudarzi, M.

Publications and source records attributed to Moazami Goudarzi, M..

4 recordsLinked to original sources

The predictive power of intrinsic timescale during the perceptual decision-making process across the mouse brain

Across the cortical hierarchy, single neurons are characterized by differences in the extent to which they can sustain their firing rate over time (i.e., their "intrinsic timescale"). Previous studies have demonstrated that neurons in a given brain region mostly exhibit either short or long intrinsic timescales. In this study, we sought to identify populations of neurons that accumulate information over different timescales in the mouse brain and to characterize their functions in the context of a visual discrimination task. Thus, we separately examined the neural population dynamics of neurons with long or short intrinsic timescales across different brain regions. More specifically, we looked at the decoding performance of these neural populations aligned to different task variables (stimulus onset, movement). Taken together, our population-level findings support the hypothesis that long intrinsic timescale neurons encode abstract variables related to decision formation. Furthermore, we investigated whether there was a relationship between how well a single neuron represents the animals choice or stimuli and their intrinsic timescale. We did not observe any significant relationship between the decoding of these task variables and a single neurons intrinsic timescale. In summary, our findings support the idea that the long intrinsic timescale population of neurons, which appear at different levels of the cortical hierarchy, are primarily more involved in representing the decision variable.

neuroscience↗

Online spike sorting via deep contractive autoencoder

Spike sorting - the process of separating spikes from different neurons - is often the first and most critical step in the neural data analysis pipeline. Spike-sorting techniques isolate a single neurons activity from background electrical noise based on the shapes of the waveforms (WFs) obtained from extracellular recordings. Despite several advancements in this area, an important remaining challenge in neuroscience is online spike sorting, which has the potential to significantly advance basic neuroscience research and the clinical setting by providing the means to produce real-time perturbations of neurons via closed-loop control. Current approaches to online spike sorting are not fully automated, are computationally expensive and are often outperformed by offline approaches. In this paper, we present a novel algorithm for fast and robust online classification of single neuron activity. This algorithm is based on a deep contractive autoencoder (DCAE) architecture. DCAEs are deep neural networks that can learn a latent state representation of their inputs. The main advantage of DCAE approaches is that they are less sensitive to noise (i.e., small perturbations in their inputs). We therefore reasoned that they can form the basis for robust online spike sorting algorithms. Overall, our DCAE-based online spike sorting algorithm achieves over 90% accuracy at sorting previously-unseen spike waveforms. Moreover, our approach produces superior results compared to several state-of-the-art offline spike-sorting procedures.

paleontology↗

Brain-behaviour relationships in the perceptual decision-making process through cognitive processing stages

Perceptual decision making, as a process of detecting and categorizing information, has been studied extensively over the last two decades. In this study, we investigated the neural characterization of the whole decision-making process by discovering the information processing stages. Such that, the timing and the neural signature of the processing stages were identified for individual trials. The association of stages duration with the stimulus coherency and spatial prioritization factors also revealed the importance of the evidence accumulation process on the speed of the whole decision-making process. We reported that the impact of the stimulus coherency and spatial prioritization on the neural representation of the decision-making process was consistent with the behavioral characterization as well. This study demonstrated that uncovering the cognitive processing stages provided more insights into the decision-making process.

neuroscience↗

Neuronal rhythms orchestrate cell assembles to distinguish perceptual categories

Categories are reflected in the spiking activity of neurons. However, how neurons form ensembles for categories is unclear. To address this, we simultaneously recorded spiking and local field potential (LFP) activity in the lateral prefrontal cortex (lPFC) of monkeys performing a delayed match to category task with two independent category sets (Animals: Cats vs Dogs; Cars: Sports Cars vs Sedans). We found stimulus and category information in alpha and beta band oscillations. Different category distinctions engaged different frequencies. There was greater spike field coherence (SFC) in alpha ([~]8-14 Hz) for Cats and in beta ([~]16-22 Hz) for Dogs. Cars showed similar differences, albeit less pronounced: greater alpha SFC for Sedans and greater beta SFC for Sports Cars. Thus, oscillatory rhythms can help coordinate neurons into different ensembles. Engagement of different frequencies may help differentiate the categories.

animal behavior and cognition↗